"""simple docstring"""



import argparse
from copy import deepcopy

import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    set_seed,
)



def 	_lowerCamelCase(	):
       __a       =    argparse.ArgumentParser()
       parser.add_argument("--model_ckpt"   ,    type=a   ,    default="microsoft/unixcoder-base-nine"				)
       parser.add_argument("--num_epochs"   ,    type=a   ,    default=5				)
       parser.add_argument("--batch_size"   ,    type=a   ,    default=6				)
       parser.add_argument("--gradient_accumulation_steps"   ,    type=a   ,    default=1				)
       parser.add_argument("--freeze"   ,    type=a   ,    default=a				)
       parser.add_argument("--learning_rate"   ,    type=a   ,    default=5E-4				)
       parser.add_argument("--seed"   ,    type=a   ,    default=0				)
       parser.add_argument("--lr_scheduler_type"   ,    type=a   ,    default="cosine"				)
       parser.add_argument("--num_warmup_steps"   ,    type=a   ,    default=1_0				)
       parser.add_argument("--weight_decay"   ,    type=a   ,    default=0.01				)
       parser.add_argument("--output_dir"   ,    type=a   ,    default="./results"				)
       return parser.parse_args()


SCREAMING_SNAKE_CASE__:List[str]									=			load("""accuracy""")



def 	_lowerCamelCase(	a				):
       __a ,   __a       =    eval_pred
       __a       =    np.argmax(a   ,    axis=1				)
       return metric.compute(predictions=a   ,    references=a				)






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		lowerCamelCase						):
        super().__init__()
        __a       =    trainer






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		**lowerCamelCase						):
        if control.should_evaluate:
               __a       =    deepcopy(lowerCamelCase						)
               self._trainer.evaluate(eval_dataset=self._trainer.train_dataset    ,		metric_key_prefix="train"						)
               return control_copy



def 	_lowerCamelCase(	):
       __a       =    get_args()
       set_seed(args.seed				)

       __a       =    load_dataset("codeparrot/codecomplex"   ,    split="train"				)
       __a       =    dataset.train_test_split(test_size=0.2				)
       __a       =    train_test["test"].train_test_split(test_size=0.5				)
       __a       =    DatasetDict(
           {
               "train": train_test["train"],
               "test": test_validation["train"],
               "valid": test_validation["test"],
           }				)

       print("Loading tokenizer and model"				)
       __a       =    AutoTokenizer.from_pretrained(args.model_ckpt				)
       __a       =    tokenizer.eos_token
       __a       =    AutoModelForSequenceClassification.from_pretrained(args.model_ckpt   ,    num_labels=7				)
       __a       =    model.config.eos_token_id

       if args.freeze:
              for param in model.roberta.parameters():
                     __a       =    False

       __a       =    ClassLabel(num_classes=7   ,    names=list(set(train_test_validation["train"]["complexity"]				)				)				)

       def tokenize(a				):
              __a       =    tokenizer(example["src"]   ,    truncation=a   ,    max_length=1_0_2_4				)
              __a       =    labels.straint(example["complexity"]				)
              return {
                  "input_ids": inputs["input_ids"],
                  "attention_mask": inputs["attention_mask"],
                  "label": label,
              }

       __a       =    train_test_validation.map(
           a   ,    batched=a   ,    remove_columns=train_test_validation["train"].column_names   ,    )
       __a       =    DataCollatorWithPadding(tokenizer=a				)

       __a       =    TrainingArguments(
           output_dir=args.output_dir   ,    learning_rate=args.learning_rate   ,    lr_scheduler_type=args.lr_scheduler_type   ,    evaluation_strategy="epoch"   ,    save_strategy="epoch"   ,    logging_strategy="epoch"   ,    per_device_train_batch_size=args.batch_size   ,    per_device_eval_batch_size=args.batch_size   ,    num_train_epochs=args.num_epochs   ,    gradient_accumulation_steps=args.gradient_accumulation_steps   ,    weight_decay=0.01   ,    metric_for_best_model="accuracy"   ,    run_name="complexity-java"   ,    report_to="wandb"   ,    )

       __a       =    Trainer(
           model=a   ,    args=a   ,    train_dataset=tokenized_datasets["train"]   ,    eval_dataset=tokenized_datasets["valid"]   ,    tokenizer=a   ,    data_collator=a   ,    compute_metrics=a   ,    )

       print("Training..."				)
       trainer.add_callback(CustomCallback(a				)				)
       trainer.train()


if __name__ == "__main__":
   main()

"""simple docstring"""



import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


SCREAMING_SNAKE_CASE__:Optional[Any]									=			logging.get_logger(__name__)






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		lowerCamelCase						):
        super().__init__()
        __a       =    nn.ModuleList(lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = False    ,		lowerCamelCase = True    ,		):
        for i, (image, scale, controlnet) in enumerate(zip(lowerCamelCase    ,		lowerCamelCase    ,		self.nets						)						):
               __a ,   __a       =    controlnet(
                   lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		)

               # merge samples
               if i == 0:
                      __a ,   __a       =    down_samples, mid_sample
               else:
                      __a       =    [
                          samples_prev + samples_curr
                          for samples_prev, samples_curr in zip(lowerCamelCase    ,		lowerCamelCase						)
                      ]
                      mid_block_res_sample += mid_sample

        return down_block_res_samples, mid_block_res_sample


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = False    ,		lowerCamelCase = None    ,		):
        __a       =    0
        __a       =    save_directory
        for controlnet in self.nets:
               controlnet.save_pretrained(
                   lowerCamelCase    ,		is_main_process=lowerCamelCase    ,		save_function=lowerCamelCase    ,		safe_serialization=lowerCamelCase    ,		variant=lowerCamelCase    ,		)

               idx += 1
               __a       =    model_path_to_save + F"_{idx}"






 @classmethod
 def 						a__   ( cls    ,		lowerCamelCase    ,		**lowerCamelCase						):
        __a       =    0
        __a       =    []

        # load controlnet and append to list until no controlnet directory exists anymore
        # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
        # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
        __a       =    pretrained_model_path
        while os.path.isdir(lowerCamelCase						):
               __a       =    ControlNetModel.from_pretrained(lowerCamelCase    ,		**lowerCamelCase						)
               controlnets.append(lowerCamelCase						)

               idx += 1
               __a       =    pretrained_model_path + F"_{idx}"

        logger.info(F"{len(lowerCamelCase						)} controlnets loaded from {pretrained_model_path}."						)

        if len(lowerCamelCase						) == 0:
               raise ValueError(
                   F"No ControlNets found under {os.path.dirname(lowerCamelCase						)}. Expected at least {pretrained_model_path + '_0'}."						)

        return cls(lowerCamelCase						)
